首页|气候变化下川西地区森林碳储量对森林管理措施和干扰的长期响应

气候变化下川西地区森林碳储量对森林管理措施和干扰的长期响应

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评估气候变化下森林管理措施对森林碳储量的长期影响对我国碳中和目标达成具有重要意义.传统森林碳储量评价方法对气候变化、火灾等干扰以及森林经营管理措施等多重影响下森林生态系统长期演替过程刻画不足,难以有效揭示多种作用因素对区域森林碳汇能力的长期综合影响.针对上述问题,以气候变化敏感区川西高山、亚高山地区为例,在探讨传统碳储量评价方法局限性的基础上,构建了考虑森林干扰的自然恢复和森林干扰-森林经营管理措施组合的人工恢复方案,结合森林景观生态模型LANDIS PRO和森林生态系统碳-水动态模型PnET-Ⅱ模拟、预测了未来气候情景下(2020-2070年)自然恢复和不同人工恢复方案下区域森林地上碳储量及碳密度的时空动态,并通过对比筛选出提升固碳能力的最佳森林管理措施.结果表明,川西地区森林林龄趋于年轻化(平均林龄40a),具有巨大碳汇提升潜力.2020-2070年,自然恢复情景下研究区森林地上碳储量将由2020年的466.99Tg增加至2070年的780.96Tg,提高了 67.23%.其中,以云杉、冷杉为主的成熟、过熟常绿针叶林是川西地区碳储量的主要贡献来源.但是自然恢复情景下川西地区森林平均碳密度在21世纪中后期停滞增长,甚至下降.而人工恢复情景下,碳密度变化趋势则有所不同.在多种森林干扰与经营管理措施组合方案中,当森林火灾干扰比例为0.01/10a和森林管理措施面积比例为0.02/10a时,川西地区森林地上碳储量提升最大且碳密度呈持续增加趋势.该情景下,2070年森林碳储量及碳密度分别将达到807.76Tg和33.33Mg/hm2,较2020年分别增加了 72.97%和12.21%.2070年人工恢复情景下森林碳储量和碳密度较于自然恢复情景下分别高3.4%和8.5%.由此可见,通过人工恢复措施优化将有助于突破川西地区森林固碳能力的自然恢复瓶颈,提升区域森林生态系统对未来气候的适应能力,促进未来气候下区域森林碳储量的持续增长.
Long-term responses of forest aboveground carbon storage to forest management measures and disturbances under climate change in the western Sichuan Province
Assessing the long-term responses of forest aboveground carbon storage(ACS)to forest management measures under climate change is vital for achieving the carbon neutrality goal in China.The traditional methods(e.g.,biomass,remote sensing,and carbon flux methods)lack the expression of the long-term succession process of forest ecosystems under the impact of climate change,forest fire,and forest management measures.They fail to disclose the joint effects of forest disturbance and management on long-term forest carbon storage in the western Sichuan Province under climate change.In order to address the limitations of biomass methods,in this study,we used the west of Sichuan Province as an example,a climate change-sensitive region,to predict forest ACS and carbon density dynamics under natural and artificial restoration scenarios(a combination of forest disturbance and forest management measures)from 2020 to 2070 by the forest landscape model(LANDIS PRO)and Photosynthesis and EvapoTranspiration-Ⅱ(PnET-Ⅱ)model.The optimal forest management measure to improve forest ACS was identified by comparing future forest carbon storage in western Sichuan Province under various forest restoration measures.Under the natural recovery scenario,the forest ACS will significantly increase from 466.99Tg in 2020 to 780.96Tg in 2070.That is a 67.23%increase in forest carbon storage.The mature and overmature evergreen coniferous forests(e.g.,spruce and fir)are the main contributors to forest ACS in the western Sichuan Province.However,forest carbon density will remain stable and even decrease after the middle 21st century under the natural restoration scenario.Contrarily,the decline in forest carbon density is reversed under artificial restoration scenarios.Among the multiple combined scenarios of forest disturbances and forest management measures,the forest ACS and carbon density are highest and consistently increase when the ratio of forest disturbance area is l%/10a and the ratio of forest management area is 2%/10a.The forest ACS and carbon density will be 807.76 Tg and 33.33 Mg/hm2 in 2070,72.97%and 12.21%higher than 2020,respectively.The forest ACS and carbon density under this artificial restoration scenario are 3.4%and 8.5%higher than that of the natural restoration scenario in 2070,respectively.Our results provide effective scientific support for adaptive forest management under climate change to reach the carbon neutrality goal.Therefore,proper artificial restoration measures can break the bottleneck of natural recovery in forest carbon storage capacity and sustain the increase in forest carbon storage under future climate in the western Sichuan Province.

climate changeforest aboveground carbon storageforest restorationforest management measuresforest disturbanceLANDIS PRO model

邓诗宇、张明芳、侯怡萍、余恩旭、李强、刘子佩、胡嘉毅、田洲、徐亚莉

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电子科技大学资源与环境学院,成都 611731

不列颠哥伦比亚大学(奥肯那根校区),加拿大不列颠哥伦比亚省 V1V1V7

中国林业科学研究院森林生态环境与自然保护研究所,北京 100091

西北农林科技大学,杨陵 712100

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气候变化 森林地上碳储量 森林恢复 森林管理措施 森林干扰 LANDIS PRO模型

2025

生态学报
中国生态学学会,中国科学院生态环境研究中心

生态学报

北大核心
影响因子:2.191
ISSN:1000-0933
年,卷(期):2025.45(1)